4 citations · 4 across the 15 of their papers we have counts for
6 papers · 1 filter
Cautious optimism for deep parameterized quantum circuits
Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performa…
Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods
Elies Gil-Fuster, Matan Ninio, Lennart Bittel +4
Optimization problems are among the leading candidates for industrially relevant quantum advantage. Decoded quantum interferometry (DQI) has been proposed to tackle approximate opt…
Provable learning separation for predicting time-evolution of quantum many-body systems
Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert +2
Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantu…
Optimal algorithmic complexity of inference in quantum kernel methods
Elies Gil-Fuster, Seongwook Shin, Sofiene Jerbi +2
Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of inference: evaluating a trained mode…
A PAC-Bayesian approach to generalization for quantum models
Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert +3
Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacit…
Simulation of noisy quantum circuits using frame representations
Janek Denzler, Jose Carrasco, Jens Eisert +1
One of the core research questions in the theory of quantum computing is to find out to what precise extent the classical simulation of a noisy quantum circuits is possible and whe…